2014 Mental Health Survey in Tech Industry

Table of Contents

1. Data Introduction

2. Data Cleaning

2.1 Null Values

2.1.1 comments
2.1.2 states
2.1.3 work interfere
2.1.4 self employed

2.2 Data Consistency

2.2.1 Gender
2.2.2 Age

3. Data Analysis & Visualizations

3.1 Age
3.2 Gender
3.3. Country
3.4 Work Interference with Mental Health based on Gender
3.5 Work Interference with Mental Health based on the Company Type (Tech/Non-Tech)
3.6 Remote Work Environment and Work Interference
3.7 Mental Health Benefits
3.8 Mental Health vs. Pysical Health
    3.8.1 Do you think that discussing a health issue with your employer would have negative consequences?
    3.8.2 Have you heard of or observed negative consequences with mental health conditions in your workplace?
3.9 Mental/Physical Health Issues in Interviews
    3.9.1 Would you bring up a mental/physical health issue with a potential employer in an interview?
3.10 Willingness to Discuss a Mental Health Issue
3.11 Word Colud on Comments

4. Machine Learning

- Cleaning
- Encoding
- Scailing & Fitting
- Logistic Regression
- K-Neighbors Classifier
- Decision Tree Classifier
- Random Forest
- Accuracy Scores

5. Conclusion

import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from collections import Counter
import matplotlib.pyplot as plt
import re
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
pd.set_option('display.max_columns', None)

survey = pd.read_csv('survey.csv')

Data Descriptions

1. Data Introduction

survey.head()

Column Name	Description
family_history	Do you have a family history of mental illness?
treatment	Have you sought treatment for a mental health condition?
work_interfere	If you have a mental health condition, do you feel that it interferes with your work?
no_employees	How many employees does your company or organization have?
remote_work	Do you work remotely (outside of an office) at least 50% of the time?
tech_company	Is your employer primarily a tech company/organization?
benefits	Does your employer provide mental health benefits?
care_options	Do you know the options for mental health care your employer provides?
wellness_program	Has your employer ever discussed mental health as part of an employee wellness program?
seek_help	Does your employer provide resources to learn more about mental health issues and how to seek help?
anonymity	Is your anonymity protected if you choose to take advantage of mental health or substance abuse treatment resources?
leave	How easy is it for you to take medical leave for a mental health condition?
mentalhealthconsequence	Do you think that discussing a mental health issue with your employer would have negative consequences?
physhealthconsequence	Do you think that discussing a physical health issue with your employer would have negative consequences?
coworkers	Would you be willing to discuss a mental health issue with your coworkers?
supervisor	Would you be willing to discuss a mental health issue with your direct supervisor(s)?
mentalhealthinterview	Would you bring up a mental health issue with a potential employer in an interview?
physhealthinterview	Would you bring up a physical health issue with a potential employer in an interview?
mentalvsphysical	Do you feel that your employer takes mental health as seriously as physical health?
obs_consequence	Have you heard of or observed negative consequences for coworkers with mental health conditions in your workplace?
comments	Any additional notes or comments

	Timestamp	Age	Gender	Country	state	self_employed	family_history	treatment	work_interfere	no_employees	remote_work	tech_company	benefits	care_options	wellness_program	seek_help	anonymity	leave	mental_health_consequence	phys_health_consequence	coworkers	supervisor	mental_health_interview	phys_health_interview	mental_vs_physical	obs_consequence	comments
0	2014-08-27 11:29:31	37	Female	United States	IL	NaN	No	Yes	Often	6-25	No	Yes	Yes	Not sure	No	Yes	Yes	Somewhat easy	No	No	Some of them	Yes	No	Maybe	Yes	No	NaN
1	2014-08-27 11:29:37	44	M	United States	IN	NaN	No	No	Rarely	More than 1000	No	No	Don't know	No	Don't know	Don't know	Don't know	Don't know	Maybe	No	No	No	No	No	Don't know	No	NaN
2	2014-08-27 11:29:44	32	Male	Canada	NaN	NaN	No	No	Rarely	6-25	No	Yes	No	No	No	No	Don't know	Somewhat difficult	No	No	Yes	Yes	Yes	Yes	No	No	NaN
3	2014-08-27 11:29:46	31	Male	United Kingdom	NaN	NaN	Yes	Yes	Often	26-100	No	Yes	No	Yes	No	No	No	Somewhat difficult	Yes	Yes	Some of them	No	Maybe	Maybe	No	Yes	NaN
4	2014-08-27 11:30:22	31	Male	United States	TX	NaN	No	No	Never	100-500	Yes	Yes	Yes	No	Don't know	Don't know	Don't know	Don't know	No	No	Some of them	Yes	Yes	Yes	Don't know	No	NaN